Association of Screen Time with Sleep Duration in School-Aged Children; a Nationwide Propensity Score‑Matched Analysis: The CASPIAN‑V Study

Background: This study aims to determine the association of sleep duration with screen time in children and adolescents. Study design: A matched case-control study. Methods: This nationwide study was conducted in 2015 among 14,274 students aged 7-18 years, and one of their parents who lived in 30 provinces in Iran. Data collection was performed using questionnaires and physical examination. Watching television and working with computer were categorized into two groups (<2 h/day and ≥2 h). Moreover short sleep duration was defined as sleep duration ≤ 8 h/day. The analysis was conducted based on the propensity score using a matched case– control study design. Data analysis was performed by a conditional logistic regression. Results: Overall, 14,274 students and one of their parents completed the survey (participation rate: 99%). Mean (standard deviation) age of students was 12.3 (3.2) years, and the frequency of short sleep was (4672) 33.5% .In total, 54.3% of students watched TV ≥2 h/day and 9% of those used a computer ≥2 h/day in their leisure time. In the multivariate model, Individuals who watched TV ≥2 h/day had significantly higher odds of short sleep (OR 1.13, 95% CI 1.03, 1.24), and individuals who worked with computer ≥2 h/day had significantly higher odds of short sleep (OR 1.65, 95% CI 1.40, 1.94). Conclusion: This study revealed significant association between watching TV and using computer during leisure time with insufficient sleep. Public awareness should be provided regarding the adverse effects of screen use on sleep. Developing special guide lines and educational school programs on restricting screen time and increasing physical activities should be considered as a health priority for children and adolescents.


Introduction
leep is a biological and psychological necessity. Adequate sleeping, especially in childhood and adolescence have an important influence on cognitive abilities, mood and performance 1 .
Sleep problems are common among school ages. It is estimated that approximately 25 percent of children will suffer from a sleep problem at some point during childhood. About 30 percent of school-aged children and 40 percent of adolescents have sleep difficulties 2 .
Sleep loss has multiple sequels on cognitive and emotional states of children and adolescents. Evidences show that inadequate sleep in childhood leading to attention disturbance, poor decision-making and decreased memory, which may contribute to a poorer academic success 3,4 in addition to increased risk of obesity, insulin resistance and hypertension [5][6][7] . Mental effects of sleep disturbance among adolescents include depression, suicidal ideation and emotional deregulation [8][9][10] .
Sleep demands change across different ages. Even though there is inter individual variations, the latest recommendations of the American Academy of Sleep Medicine state that the appropriate sleep duration among 6 to 12 years old is 9 to 12 hours and in teenagers, between 13 to 18 years, is 8 to 10 hours per day 11 . S Sleep patterns change during life time. In adolescence, from age 10 to 19 years, sleep changes may accrue due to verity of internal and external factors. Beside changes in biological clock and social obligations and academic demands, availability of television, video games, computer and telephone alter the sleep patterns in adolescents 12 .
Studies have shown negative effects of television on physical and behavioral developments of children and adolescents. Sleep disturbance manifest as irregular sleep habits, shorter sleep duration, and sleep disorders .Use of computer and mobile phones correlates with interrupted sleep and sleep difficulties 13 .
In recent years we have witnessed a rapid increase in the availability and use of electronic devices such as television and portable electronic devices. According to Iran statistics center, more than 98 percent of Iranian families have a television and about 97 percent of them own a phone or cell phone and the number of personal computers is about 10.5 million. These changes have made a great concern of rapidly growing use of screens on health issues, especially among school-aged and adolescent. This study aims to detect the association between sleep duration and screen time in a large sample of pediatric population.

Study population
Data of this matched case-control study were obtained from the fifth phase of a national project entitled "Childhood and Adolescence Surveillance and Prevention of Adult Noncommunicable disease" (2014-2015). The Protocol details of the CASPIAN-V study have been described previously 2 , and here we present it in brief.
This national research was conducted as a Multicenter cross sectional study on 7-18 year old students with the Cooperation and coordination of the Ministry of Health and Medical Education, Ministry of Education, Child Development Research Center (CDRC) at the Isfahan University of Medical Sciences, and Endocrinology and Metabolism Research Institute (EMRI) at the Tehran University of Medical Sciences. The protocol of study was confirmed by the Ethics Committee of Isfahan University of Medical Sciences .Verbal and written consent was obtained from the students and their parents, respectively. The students were randomly selected from urban and rural areas in 30 provinces of Iran (48 clusters of 10 people in each province) by a stratified, multi-stage cluster sampling design. In total 14,400 people were surveyed in 30 provinces of the country.

Data collection
Data were collected using a structured questionnaire, clinical examinations and anthropometric measurements .The internal consistency of the questionnaire was Confirmed in the previous studies of Caspian with the overall Cranach's alpha value of 0.93 and range of 0.92 to 0.97, and the reliability was approved as 0.94 by test-retest measurement 15 . Students' demographic characteristics, healthy behaviors (physical activity, time spent watching TV, using computers, sleep duration and homework) were asked through students' questionnaires and some complementary data on family characteristics, number of children in the family, birth order, parental age and socioeconomic variables questioned through parents' questionnaires.
Also, a number of questions about some mental disorders (1-During the past 12 months, did you ever feel sad or hopeless so that you cannot do your daily activity? 2-During the past 6 months, did you experience Early anger and irritability? 3-During the past 6 months, did you experience anxiety?) were asked from the students.

Socioeconomic status
The socioeconomic status (SES) score was calculated using the Principle component analysis method based on parents' education, parents' job, type of school (private or public), type of house, having personal computer in house and possessing private car. Provided measure was categorized as low, medium and high socioeconomic status.
Physical activity (hours/day) was estimated using a validated questionnaire. Students were asked about 7-day recalls of sports or activities in and out of school activities that led to sweating and increasing in breathing or heart rate. All physical activity items were summarized in a main variable through principal component analysis (PCA) methods. This main variable was categorized into tertiles. The first tertile was defined as a low, second tertile as a medium and third tertile as a high PA 16 .

Sedentary behaviors
The time spent watching TV and using computers during leisure time was assessed by a questionnaire. The students were asked to report number of hours per day spent on watching TV, PC and electronic games. Watching television and working with computer were categorized into two groups (<2 h/day: 0 and ≥2 h/day:1) 17 .
Moreover, students reported that they usually sleep several hours on weekdays and on weekends. For each student the weighted average hours of sleeping per day was calculated. Sleep duration was then considered as a dichotomous variable. Short sleep duration was defined as sleep duration ≤ 8 h/day 18 . (Long sleepers: 0, short sleepers: 1)

Anthropometric measurements
A trained team of health care providers measured the weight and height of students under standard protocols using calibrated instruments 15 . Height was measured without shoes to the nearest 0.1 cm using a non-elastic tape meter and Students' weights were measured on a scale placed on a flat ground to the nearest 0.1 kg. BMI was calculated as weight (kg) divided by square of height (m 2 ).

Statistical analysis and matching based on Propensity Score
In order to describe and initial analysis quantitative and qualitative variables based on short sleep, independent t-test, Chi-square test were used, respectively. The quantitative variables are reported as mean and standard deviation (SD) and the qualitative variables as frequency and percentage.
Propensity Score (PS) was calculated based on conditional logistic model with potential Confounding variables (age, sex, living area, SES, PA, homework, BMI, Birth order, number of children in the family , parental age and Some mental disorders (During the past 12 months, did you ever feel sad or hopeless so that you cannot do your daily activity? , During the past 6 months, did you experience Early anger and irritability? , During the past 6 months, did you experience anxiety ? ).Two groups (short sleepers and long sleepers) were matched based on 1:1 matching method without replacement by the score. Results of conditional logistic regression were reported as odds ratios (OR) and 95% confidence intervals (CI).
In addition, we have also implemented Logistic regression analysis. STATA-10 (Stata Corp, College Station, Texas, USA) was used to analyze the data set. The P-value less than 0.05 were considered as significant.
The mean (SD) sleep duration of students was 8.57(1.23) hours in day and the frequency of short sleep was(4672 )33.5% (sleep duration ≤ 8 h/day).In total, 54.3% of students watched TV ≥2 h/day, 9% of those used a computer ≥2 h/day in their leisure time.
The PS was calculated according to conditional logistic model with potential confounders. Short sleepers and long sleepers were matched using one to one PS matching method. Table 1 shows, the distribution of characteristics of subjects in both groups prior to and after matching. The total number of participants in the pre-matching sample was 13942 (long sleepers: 9270, short sleepers: 4672). In contrast, the total number of participants in the postmatching sample was 7566 (long sleepers: 3783, short sleepers: 3783).
The mean of Standardized Bias percentages before and after matching were calculated. After matching, the mean of Standardized Bias percentage was reduced from 10.3 to 1.2%which shows the high quality of the matching and ensures that the distribution of the variables was not statistically significant between two groups (Figure 1).
Results of multivariate conditional logistic regression for association between watching TV and working computer with short sleep are presented in Table 2.Individuals who watched TV ≥2 h/day had significantly higher odds of short sleep (OR 1.13, 95% CI 1.03, 1.24). Individuals who worked computer ≥2 h/day had significantly higher odds of short sleep (OR 1.65, 95% CI 1.40, 1.94).
In addition, Multiple Logistic regression analysis after controlling the variables such (age, sex, living area, SES, PA, homework, BMI, Birth order, number of children in the family, parental age and Some mental disorders (During the past 12 months, did you ever feel sad or hopeless so that you cannot do your daily activity?, During the past 6 months, did you experience early anger and irritability?, During the past 6 months, did you experience anxiety? ) showed that individuals who watched TV ≥2 h/day had significantly higher odds of short sleep (OR 1.09, 95% CI 1.00, 1.18).Findings of this study also showed that individuals who worked computer ≥2 h/day had significantly higher odds of short sleep (OR 1.73, 95% CI 1.51, 1.98).

Discussion
The main objective of this study was to assess the relationship between screen-time and sleep parameters such as sleep duration among school-aged and adolescents. Findings showed that watching TV or working with computer more than 2 h/day was associated with short sleep duration. Utilization of electronic devices is prevalent during adolescence and previous studies revealed the negative association of using TV with sleep duration in addition to delay in bed time and wake up time among adolescents 21 . Over use of computer would lead to sleep problems and bed time reduction 3,24 . In conclusion, using electronic media has been related to sleep reduction and bed time delay, based on a review of literature Bright light exposure which is the consequence of electronic media devices may interfere with sleep by inducing misalignment between the sleep-wake cycle and the internal clock 25 . It has been found that Light exposure in the evening, increase alertness and arousal levels, suppress melatonin production 30 and induce phase delay in the circadian clock such as delay in sleep time 31 .
Study limitations and strengths: This study is a cross sectional study and we cannot confirm the casual relationship between screen time and sleep difficulties. Data were based on self-reports so recall biases may interfere with results. Other variables such as psychological status and physical activities that have possible effects on sleep were not analyzed. The main strength of this study is the large sample size and to our knowledge it is the largest study of screen time relationship with sleep deficits in pediatric population and participants were gathered from different parts of Iran and included various socio demographic features. Moreover the matching method based on propensity score was used as the method for controlling a large number of probably confounding variables. Publication of results recently based on this method has been increased dramatically 32 .This method reduced the possibility of confounding and made the results more reliable.

Conclusion
According to this study there is a strong relationship between screen time and sleep difficulties. Because of importance of adequate sleep from one hand and high rate of screen use on the other hand, policy makers and individuals should be informed to limit the screen time. Special guide lines for healthy use of screens should be developed according to different ages. School programs have an important role in promoting health issues and are recommended for students and parents to give them more understanding regarding the use of television and computer. Parents have to monitor and control leisure time activities of their children and consider more active and creative ways to spend time together.